Optimal windows for obtaining optical data for characterization of tissue samples

ABSTRACT

The invention provides methods for determining a characteristic of a tissue sample, such as a state of health, using spectral data and/or images obtained within an optimal period of time following the application of a chemical agent to the tissue sample. The invention provides methods of determining such optimal windows of time. Similarly, the invention provides methods of determining other criteria for triggering the acquisition of an optical signal for classifying the state of health of a region of a tissue sample.

PRIOR APPLICATIONS

[0001] The present application claims the benefit of U.S. ProvisionalPatent Application Serial No. 60/394,696, filed Jul. 9, 2002, which ishereby incorporated by reference.

FIELD OF THE INVENTION

[0002] The invention relates generally to spectroscopic methods. Moreparticularly, the invention relates to the diagnosis of disease intissue using spectral analysis and/or image analysis.

BACKGROUND OF THE INVENTION

[0003] Spectral analysis is used to diagnose disease in tissue. Forexample, data from spectral scans performed on the tissue of a patientare used to screen tissue for disease. Some diagnostic proceduresinclude the application of a chemical contrast agent to the tissue inorder to enhance the image and/or spectral response of the tissue fordiagnosis. In an acetowhitening procedure, acetic acid is used as thecontrast agent. Use of a contrast agent enhances the difference betweendata obtained from normal tissue and data obtained from abnormal ordiseased tissue.

[0004] Current techniques do not suggest an optimal time periodfollowing application of a contrast agent within which to obtainspectral and/or image data for the diagnosis of disease, nor do currenttechniques suggest how such an optimal time period could be determined.

SUMMARY OF THE INVENTION

[0005] The invention provides optimal criteria for selecting spectraland/or image data from tissue that has been treated with a contrastagent for disease screening. In particular, it has been discovered thatthe sensitivity and specificity. of optical diagnostic screening isimproved by obtaining optical data at optimal time points afterapplication of a contrast agent.

[0006] Accordingly, methods of the invention provide optimal windows intime for obtaining spectral data from tissue that has been treated witha contrast agent in order to improve the results of disease screening.The invention further provides methods for identifying such windows inthe context of any optical diagnostic screen. Additionally, theinvention provides methods for disease screening using kinetic dataobtained across multiple diagnostic windows. Methods of the inventionallow an optical diagnostic test to focus on data that will produce thehighest diagnostic sensitivity and specificity with respect to thetissue being examined. Thus, the invention allows the identification ofspecific points in time after treatment of a tissue when spectral and/orimage data most accurately reflects the health of the tissue beingmeasured.

[0007] Time windows for observing selected spectral data may bedetermined empirically or from a database of known tissue responses tooptical stimulation. For example, in one aspect the invention comprisesbuilding and using classification models to characterize the state ofhealth of an unknown tissue sample from which optical signals areobtained. As used herein, an optical signal may comprise a discrete orcontinuous electromagnetic signal or any portion thereof, or the datarepresenting such a signal. Essentially, optical diagnostic windows arebased upon the points at which classification models perform best. Inpractice, optimal diagnostic windows of the invention may bepredetermined segments of time following application of a contrast agentto a tissue. Optimal diagnostic windows may also be points in time atwhich an optical measurement meets a predetermined threshold or fallswithin a predetermined range, where the optical measurement representsthe change of an optical signal received from the tissue followingapplication of a contrast agent. For example, a window may be selectedto include points in time at which the change in optical signalintensity from an initial condition is maximized. Finally, the opticalmeasurement upon which a window is based may also reflect the rate ofchange in a spectral property obtained from the tissue.

[0008] In a preferred embodiment, optimal windows are determined byobtaining optical signals from reference tissue samples with knownstates of health at various times following application of a contrastagent. For example, one embodiment comprises obtaining a first set ofoptical signals from tissue samples having a known disease state, suchas CIN 2/3 (grades 2 and/or 3 cervical intraepithelial neoplasia);obtaining a second set of optical signals from tissue samples having adifferent state of health, such as non-diseased; and categorizing eachoptical signal into “bins” according to the time it was obtained inrelation to the time of application of contrast agent. The opticalsignal may comprise, for example, a reflectance spectrum, a fluorescencespectrum, a video image intensity signal, or any combination of these.

[0009] A measure of the difference between the optical signalsassociated with the two types of tissue is then obtained, for example,by determining a mean signal as a function of wavelength for each of thetwo types of tissue samples for each time bin, and using adiscrimination function to determine a weighted measure of differencebetween the two mean optical signals obtained within a given time bin.This provides a measure of the difference between the mean opticalsignals of the two categories of tissue samples—diseased andhealthy—weighted by the variance between optical signals of sampleswithin each of the two categories.

[0010] In one embodiment, the invention further comprises developing aclassification model for each time bin. After determining a measure ofdifference between the tissue types in each bin, an optimal window oftime for differentiating between tissue types is determined byidentifying at least one bin in which the measure of difference betweenthe two tissue types is substantially maximized. For example, an optimalwindow of time may be chosen to include every time bin in which therespective classification model provides an accuracy of 70% or greater.Here, the optimal window describes a period of time followingapplication of a contrast agent in which an optical signal can beobtained for purposes of classifying the state of health of the tissuesample with an accuracy of at least 70%.

[0011] An analogous embodiment comprises determining an optimalthreshold or range of a measure of change of an optical signal to use inobtaining (or triggering the acquisition of) the same or a differentsignal for predicting the state of health of the sample. Instead ofdetermining a specific, fixed window of time, this embodiment includesdetermining an optimal threshold of change in a signal, such as a videoimage whiteness intensity signal, after which an optical signal, such asa diffuse reflectance spectrum and/or a fluorescence spectrum, can beobtained to accurately characterize the state of health or othercharacteristic of the sample. An embodiment includes monitoringreflectance and/or fluorescence at a single or multiple wavelength(s),and upon reaching a threshold change from the initial condition,obtaining a full reflectance and/or fluorescence spectrum for use indiagnosing the region of tissue. This method allows for reduced dataretrieval and monitoring since, in an embodiment, it involves continuoustracking of a single, partial-spectrum or discrete-wavelength “trigger”signal (instead of multiple, full-spectrum scans), followed by theacquisition of one or more spectral scans for use in diagnosis.Alternatively, the trigger may include more than one discrete-wavelengthor partial-spectrum signal. The diagnostic data obtained will generallybe more extensive than the trigger signal, and may include one or morecomplete sets of spectral data. The measure of change used to triggerobtaining one or more optical signals for tissue classification may be aweighted measure, and/or it may be a combination of measures of changeof more than one signal. The signal(s) used for tissueclassification/diagnosis may comprise one or more reflectance,fluorescence, and/or video signals. In one embodiment, two reflectancesignals are obtained from the same region in order to provide aredundant signal for use when one reflectance signal is adverselyaffected by an artifact such as glare or shadow. Use of multiple typesof classification signals may provide improved diagnostic accuracy overthe use of a single type of signal. In one embodiment, a reflectance,fluorescence, and a video signal from a region of a tissue sample areall used in the classification of the region.

[0012] In a further embodiment, instead of determining an optimalthreshold or range of a measure of change of an optical signal, anoptimal threshold or range of a measure of the rate of change of anoptical signal is determined. For example, the rate of change ofreflectance and/or fluorescence is monitored at a single or multiplewavelength(s), and upon reaching a threshold rate of change, a fullreflectance spectrum and/or fluorescence spectrum is acquired for use indiagnosing the region of tissue. The measure of rate of change used totrigger obtaining one or more optical signals for tissue classificationmay be a weighted measure, and/or it may be combination of measures ofchange of more than one signal. For example, the measured rate of changemay be weighted by an initial signal intensity.

[0013] The invention also provides methods of disease screening usingkinetic data from optical signals obtained at various times followingapplication of a contrast agent. These methods comprise techniques forusing specific features of fluorescence and diffuse reflectance spectrafrom reference cervical tissue samples of known states of health inorder to diagnose a region of a tissue sample. These techniques allowmonitoring of a particular optical signal from a test sample during aspecified period of time following application of contrast agent toobtain pertinent kinetic data for characterizing the sample. Forexample, two or more time-separated measures of video intensity,fluorescence, and/or reflectance are obtained for a test sample at timesbetween which it is known that an increase or decrease indicative of agiven state of health occurs. It is therefore possible to determinewhether this increase or decrease has occurred for the test sample,thereby indicating the sample may have a given state of health.Alternatively or additionally, a video, reflectance, and/or fluorescencesignal from a test sample may be monitored over time to determine a timeat which the signal reaches a maximum or minimum value. The timefollowing application of contrast agent at which this minimum or maximumis reached can then be used to determine indication of a disease statein the test sample.

[0014] In one embodiment, data used as a baseline in determining anincrease, decrease, maximum, or minimum as discussed above is notobtained before, but is obtained immediately following application ofcontrast agent to the tissue. In one case, the time period immediatelyfollowing application of contrast agent is about ten seconds, and inanother case, it is about five seconds, although other time periods arepossible. This may be done to avoid error caused by movement of tissueor movement of the optical signal detection device upon application ofcontrast agent, particularly where such movement is not otherwisecompensated for. Movement of tissue may cause error where a change froman initial condition is being monitored and the region of the tissuecorresponding to the location at which the initial signal was obtainedshifts following application of contrast agent.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The objects and features of the invention can be betterunderstood with reference to the drawings described below, and theclaims. The drawings are not necessarily to scale, emphasis insteadgenerally being placed upon illustrating the principles of theinvention. In the drawings, like numerals are used to indicate likeparts throughout the various views.

[0016]FIG. 1A shows a graph depicting mean fluorescence spectra beforeapplication of acetic acid and at various times following theapplication of acetic acid for NED tissue (no evidence of disease,confirmed by pathology).

[0017]FIG. 1B shows a graph depicting mean reflectance spectra beforeapplication of acetic acid and at various times following theapplication of acetic acid for NED tissue (no evidence of disease,confirmed by pathology).

[0018]FIG. 2A shows a graph depicting mean fluorescence spectra beforeapplication of acetic acid and at various times following theapplication of acetic acid for CIN 2/3 tissue (grades 2 and/or 3cervical intraepithelial neoplasia).

[0019]FIG. 2B shows a graph depicting mean reflectance spectra beforeapplication of acetic acid and at various times following theapplication of acetic acid for CIN 2/3 tissue (grades 2 and/or 3cervical intraepithelial neoplasia).

[0020]FIG. 3A shows a graph depicting fluorescence intensity at threedifferent wavelengths relative to pre-AA (fluorescence beforeapplication of acetic acid) as a function of time following applicationof acetic acid for NED tissue.

[0021]FIG. 3B shows a graph depicting reflectance at three differentwavelengths relative to pre-AA (reflectance before application of aceticacid) as a function of time following application of acetic acid for NEDtissue.

[0022]FIG. 3C shows a graph depicting fluorescence intensity at threedifferent wavelengths relative to pre-AA (fluorescence beforeapplication of acetic acid) as a function of time following applicationof acetic acid for CIN 2/3 tissue.

[0023]FIG. 3D shows a graph depicting reflectance at three differentwavelengths relative to pre-AA (reflectance before application of aceticacid) as a function of time following application of acetic acid for CIN2/3 tissue.

[0024]FIG. 4A shows a graph depicting reflectance relative to pre-AA at425 nm as a function of time following application of acetic acid forvarious tissue types.

[0025]FIG. 4B shows a graph depicting fluorescence relative to pre-AA at460 nm as a function of time following application of acetic acid forvarious tissue types.

[0026]FIG. 5 shows a series of graphs depicting mean reflectance spectrafor CIN 2/3 and non-CIN 2/3 (NED and CIN 1) tissues at a time prior toapplication of acetic acid, at a time corresponding to maximumwhitening, and at a time corresponding to the latest time at which datawas obtained.

[0027]FIG. 6 shows a graph depicting the reflectance discriminationfunction spectra useful for differentiating between CIN 2/3 and non-CIN2/3 (NED and CIN 1) tissues.

[0028]FIG. 7 shows a graph depicting the performance of two LDA (lineardiscriminant analysis) models as applied to reflectance data obtained atvarious times following application of acetic acid; one of the models isbased on data obtained between 60 and 80 seconds following applicationof acetic acid, and the other model is based on data obtained between160 and 180 seconds following application of acetic acid.

[0029]FIG. 8 shows a series of graphs depicting mean fluorescencespectra for CIN 2/3 and non-CIN 2/3 (NED and CIN 1) tissues at a timeprior to application of acetic acid, at a time corresponding to maximumwhitening, and at a time corresponding to the latest time at which datawas obtained.

[0030]FIG. 9 shows a graph depicting the fluorescence discriminationfunction spectra useful for differentiating between CIN 2/3 and non-CIN2/3 (NED and CIN 1) tissues.

[0031]FIG. 10 shows a graph depicting the performance of two LDA (lineardiscriminant analysis) models as applied to fluorescence data obtainedat various times following application of acetic acid; one of the modelsis based on data obtained between 60 and 80 seconds followingapplication of acetic acid, and the other model is based on dataobtained between 160 and 180 seconds following application of aceticacid.

[0032]FIG. 11 shows a graph depicting the performance of three LDAmodels as applied to data obtained at various times followingapplication of acetic acid.

[0033]FIG. 12A shows a graph depicting the determination of an optimaltime window for obtaining diagnostic optical data using an opticalamplitude trigger.

[0034]FIG. 12B shows a graph depicting the determination of an optimaltime window for obtaining diagnostic data using a rate of change of meanreflectance signal trigger.

DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENT

[0035] The invention relates to methods for determining a characteristicof a tissue sample using spectral data and/or images obtained within anoptimal window of time following the application of a chemical agent tothe tissue sample. The invention provides methods of determining optimalwindows of time. Similarly, the invention provides methods ofdetermining criteria, based on a spectral amplitude or rate of amplitudechange, for triggering the acquisition of an optical signal forclassifying tissue. Finally, the invention comprises methods ofdiagnosing a tissue sample using spectral data and/or images obtainedwithin an optimal window.

[0036] Application of the invention allows the diagnosis of regions of atissue sample using various features of the time response offluorescence and/or reflectance spectra following the application of acontrast agent such as acetic acid. For example, it is possible todiagnose a region of a tissue sample by determining a time at which aminimum value of fluorescence spectral intensity is reached followingapplication of a contrast agent.

[0037] Methods of the invention are also used to analyze tissue samples,including cervical tissue, colorectal tissue, gastroesophageal tissue,urinary bladder tissue, lung tissue, or other tissue containingepithelial cells. The tissue may be analyzed in vivo or ex vivo, forexample. Tissue samples are generally divided into regions, each havingits own characteristic. This characteristic may be a state of health,such as intraepithelial neoplasia, mature and immature metaplasia,normal columnar epithelia, normal squamous epithelia, and cancer.Chemical contrast agents which are used in practice of the inventioninclude acetic acid, formic acid, propionic acid, butyric acid, Lugol'siodine, Shiller's iodine, methylene blue, toluidine blue, indigocarmine, indocyanine green, fluorescein, and combinations comprisingthese agents. In embodiments where acetic acid is used, concentrationsbetween about 3 volume percent and about 6 volume percent acetic acidare typical, although in some embodiments, concentrations outside thisrange may be used. In one embodiment, a 5 volume percent solution ofacetic acid is used as contrast agent.

[0038] Optical signals used in practice of the invention comprise, forexample, fluorescence, reflectance, Raman, infrared, and video signals.Video signals comprise images from standard black-and-white or color CCDcameras, as well as hyperspectral imaging signals based on fluorescence,reflectance, Raman, infrared, and other spectroscopic techniques. Forexample, an embodiment comprises analyzing an intensity componentindicative of the “whiteness” of a pixel in an image during anacetowhitening test.

[0039] A preferred embodiment uses optical signals obtained from tissuesamples within optimal windows of time. Obtaining an optical signal maycomprise actually acquiring a signal within an optimal window of time,or, of course, simply triggering the acquisition of an optical signalwithin an optimal window of time. The optimal window of time may accountfor a delay between the triggering of the acquisition of a signal, andits actual acquisition. An embodiment of the invention may comprisedetermining an optimal window of time in which to trigger theacquisition of an optical signal, as well as determining an optimalwindow of time in which to actually acquire an optical signal.

[0040] One embodiment comprises determining an optimum time window inwhich to obtain spectra from cervical tissue such that sites indicativeof grades 2 and 3 cervical intraepithelial neoplasia (CIN 2/3) can beseparated from non-CIN 2/3 sites. Non-CIN 2/3 sites include sites withgrade 1 cervical intraepithelial neoplasia (CIN 1), as well as NED sites(which include mature and immature metaplasia, and normal columnar andnormal squamous epithelia). Alternately, sites indicative of high gradedisease, CIN 2+, which includes CIN 2/3 categories, carcinoma in situ(CIS), and cancer, may be separated from non-high-grade-disease sites.In general, for any embodiment in which CIN 2/3 is used as a categoryfor classification or characterization of tissue, the more expansivecategory CIN 2+ may be used alternatively. One embodiment comprisesdifferentiating amongst three or more classification categories.Exemplary embodiments are described below and comprise analysis of thetime response of diffuse reflectance and/or 337-nm fluorescence spectraof a set of reference tissue samples with regions having known states ofhealth, as listed in the Appendix Table, to determine temporalcharacteristics indicative of the respective states of health. Thesecharacteristics are then used in building a model to determine a stateof health of an unknown tissue sample. Other embodiments compriseanalysis of fluorescence spectra using other excitation wavelengths,such as 380 nm and 460 nm, for example.

[0041] While the invention is particularly shown and described hereinwith reference to specific examples and specific embodiments, it shouldbe understood by those skilled in the art that various changes in formand detail may be made therein without departing from the spirit andscope of the invention.

EXAMPLE 1 Analysis of the Temporal Evolution of Spectral Data fromReference Samples with Known States of Health.

[0042] Diffuse reflectance and/or 337-nm fluorescence emission spectraare taken from cervical tissue samples that are categorized as CIN 2/3(having grades 2 and/or 3 cervical intraepithelial neoplasia), CIN 1 andNED (no evidence of disease, confirmed by pathology, including normalsquamous tissue, normal columnar tissue, immature metaplasia tissue, andmature metaplasia tissue). All spectra are filtered then placed in thetime bins indicated in Table 1. Data affected by artifacts such asglare, shadow, or obstructions may be removed and/or compensated for byusing the technique disclosed in the co-owned U.S. patent applicationentitled, “Method and Apparatus for Identifying Spectral Artifacts,”filed on Sep. 13, 2002, and identified by attorney docket numberMDS-033, the contents of which are hereby incorporated by reference.Means spectra and standard deviations are calculated for the spectra ineach time bin. Although not shown in this example, some embodiments usespectral and/or image data obtained at times greater than 180 sfollowing application of contrast agent. TABLE 1 Time bins for whichmeans spectra are calculated in an exemplary embodiment Bin Time afterapplication of Acetic Acid (s) 1 t ≦ 0 2  0 < t ≦ 40 3  40 < t ≦ 60 4 60 < t ≦ 80 5  80 < t ≦ 100 6 100 < t ≦ 120 7 120 < t ≦ 140 8 140 < t ≦160 9 160 < t ≦ 180 10 t > 180

[0043]FIGS. 1A, 1B, 2A, and 2B show mean fluorescence and reflectancespectra for exemplary healthy tissue (NED tissue—no evidence of disease,confirmed by pathology) and CIN 2/3 (grades 2 and/or 3 cervicalintraepithelial neoplasia) tissue samples. These figures demonstrate thetemporal effect of acetic acid on the spectral data. In the applicationof one embodiment, one or more characteristics of the time responsesshown in FIGS. 1A, 1B, 2A, and 2B are determined. Subsequently, the timeresponse of a sample of unknown type is obtained, and the sample is thendiagnosed according to one or more features of the response, comparedagainst those of the known sample set.

[0044]FIG. 1A shows a graph 102 depicting mean fluorescence spectra foreach of the 10 time bins 108 of Table 1 for NED tissue (no evidence ofdisease, confirmed by pathology). Mean fluorescence intensity (relativecounts/μJ) 104 is plotted as a function of wavelength (nm) 106 for eachtime bin shown in the legend 108. The curve corresponding to the firsttime bin 110 is a graph of the mean fluorescence intensity as a functionof wavelength for data collected prior to acetic acid application, andthe curve corresponding to the last time bin 128 is a graph of the meanfluorescence intensity as a function of wavelength for data collected attimes greater than 180 seconds (with an average of about 210 seconds).Each of the curves in between (112, 114, 116, 118, 120, 122, 124, 126)is a graph of the mean fluorescence intensity as a function ofwavelength for data collected in the respective time bin shown in thelegend 108. The value of N shown in the legend 108 beside each curvedenotes the number of spectra that are in the respective time bin forthis particular embodiment.

[0045]FIG. 1B shows a graph 150 depicting mean reflectance spectra foreach of the 10 time bins 108 of Table 1 for NED tissue (no evidence ofdisease, confirmed by pathology). Mean reflectance 152 is plotted as afunction of wavelength (nm) 106 for each time bin shown in the legend108. The curve corresponding to the first time bin 154 is a graph of themean reflectance as a function of wavelength for data collected prior toacetic acid application, and the curve corresponding to the last timebin 172 is a graph of the mean reflectance as a function of wavelengthfor data collected at times greater than 180 seconds (with an average ofabout 210 seconds). Each of the curves in between (156, 158, 160, 162,164, 166, 168, 170) is a graph of the mean reflectance as a function ofwavelength for data collected in the respective time bin shown in thelegend 108. The value of N shown in the legend 108 beside each curvedenotes the number of spectra that are in the respective time bin forthis particular embodiment.

[0046]FIG. 2A shows a graph 202 depicting mean fluorescence spectra foreach of the 10 time bins 204 of Table 1 for CIN 2/3 tissue (grades 2and/or 3 cervical intraepithelial neoplasia). Mean fluorescenceintensity (relative counts/μJ) 104 is plotted as a function ofwavelength (nm) 106 for each time bin shown in the legend 204. The curvecorresponding to the first time bin 206 is a graph of the meanfluorescence intensity as a function of wavelength for data collectedprior to acetic acid application, and the curve corresponding to thelast time bin 224 is a graph of the mean fluorescence intensity as afunction of wavelength for data collected at times greater than 180seconds (with an average of about 210 seconds). Each of the curves inbetween (208, 210, 212, 214, 216, 218, 220, 220) is a graph of the meanfluorescence intensity as a function of wavelength for data collected inthe respective time bin shown in the legend 204. The value of N shown inthe legend 204 beside each curve denotes the number of spectra that arein the respective time bin for this particular embodiment.

[0047]FIG. 2B shows a graph 250 depicting mean reflectance spectra foreach of the 10 time bins 204 of Table 1 for CIN 2/3 tissue (grades 2and/or 3 cervical intraepithelial neoplasia). Mean reflectance 152 isplotted as a function of wavelength (mn) 106 for each time bin shown inthe legend 204. The curve corresponding to the first time bin 254 is agraph of the mean reflectance as a function of wavelength for datacollected prior to acetic acid application, and the curve correspondingto the last time bin 272 is a graph of the mean reflectance as afunction of wavelength for data collected at times greater than 180seconds (with an average of about 210 seconds). Each of the curves inbetween (256, 258, 260, 262, 264, 266, 268, 270) is a graph of the meanreflectance as a function of wavelength for data collected in therespective time bin shown in the legend 204. The value of N shown in thelegend 204 beside each curve denotes the number of spectra that are inthe respective time bin for this particular embodiment.

EXAMPLE 2 Analysis of Optical Kinetic Data from Reference Samples withKnown States of Health

[0048] Data from FIGS. 1A, 1B, 2A, and 2B are further analyzed as shownin FIGS. 3A, 3B, 3C, and 3D. FIG. 3A shows a graph 302 depicting thetime response of fluorescence intensity relative to pre-AA (fluorescenceprior to application of acetic acid) 304 of NED tissue at 390, 460 and600 nm wavelengths following application of acetic acid. FIG. 3B shows agraph 320 depicting the time response of reflectance relative to pre-AA322 for NED tissue at 425, 500, and 630 nm wavelengths followingapplication of acetic acid. FIG. 3C shows a graph 350 depicting the timeresponse of fluorescence intensity relative to pre-AA 304 of CIN 2/3tissue at 390, 460, and 600 nm wavelengths following application ofacetic acid. FIG. 3D shows a graph 370 depicting the time response ofreflectance relative to pre-AA 322 for CIN 2/3 tissue at 425, 500, and630 nm wavelengths following application of acetic acid.

[0049] The fluorescence intensity in the NED group continues to dropover the time period studied while some recovery is seen in thefluorescence intensity of the CIN 2/3 group. FIG. 3A reveals acontinuous drop in fluorescence for the NED group over the measurementperiod at the three wavelengths. In contrast, FIG. 3C shows partialrecovery at all three wavelengths for CIN 2/3 tissue. Each of the curvesrepresenting CIN 2/3 tissue labeled 352, 354, and 356 in FIG. 3Cdemonstrates a generalized local minimum at a time from about 70 toabout 130 seconds following application of acetic acid, whereas each ofthe curves representing NED tissue labeled 310, 312, and 314 in FIG. 3Adoes not show such a local minimum.

[0050] The fluorescence and reflectance kinetics are similar for the CIN2/3 group but differ for the NED group. Partial recovery (return towardinitial condition) is noted in both the reflectance and the fluorescencecurves at all 3 wavelengths for CIN 2/3 tissue, as shown in the curveslabeled 352, 354, 356, 372, 374, and 376 in FIG. 3C and FIG. 3D.However, partial recovery is noted only in the reflectance curves forNED tissue (curves 326, 328, and 330 of FIG. 3B), while the NEDfluorescence intensities continue to drop (curves 310, 312, and 314 ofFIG. 3A).

[0051] The magnitude of change in the time response of reflectance andfluorescence data following application of acetic acid is differentbetween the CIN 2/3 group and the NED group. The relative maximum changein reflectivity at about 425 nm is about twice as large for CIN 2/3(i.e. line segment 274 in FIG. 2B) compared to non-CIN (i.e. linesegment 174 in FIG. 1B), while the maximum change for fluorescence isapproximately equivalent for CIN 2/3 and non-CIN samples. Here, themagnitude of change in the reflectance signal depends on tissue typewhile the magnitude of change in the fluorescence signal does not dependon tissue type.

[0052] The time to reach the maximum change in fluorescence is delayedfor NED spectra. This is shown by comparing curves 310, 312, and 314 ofFIG. 3A with curves 352, 354, and 356 of FIG. 3C. It is thereforepossible, for example, to use the time required to reach a minimum valueof fluorescence spectral intensity to distinguish CIN 2/3 from NEDsamples.

[0053] The fluorescence line-shape changes with time post acetic acid,particularly at later times where a valley at about 420 nm and a band atabout 510 nm become more distinct. The valley at about 420 nm is shownin FIG. 1A at reference number 130 and in FIG. 2A at reference number226, while the band at about 510 nm can be seen in FIG. 1A at referencenumber 132 and in FIG. 2A at reference number 228. One explanation forthis change is that collagen and NADH decrease tissue fluorescence andFAD increases tissue fluorescence. Upon introduction of a change in pHfrom 7 to 3.5, the fluorescence intensity of NADH decreases by a factorof two while FAD increases six-fold. Increased scattering in theepithelial layer would decrease the contribution of collagenfluorescence from the submucosal layer. Characterization of such changesin spectral curve shape is useful, for example, in distinguishing tissuetypes.

[0054] In one embodiment, an optimal window for obtaining spectraland/or image data is a period of time in which there is a peak“whitening” as seen in image and/or reflectance data. In anotherembodiment, an optimal window is a period of time in which there is apeak “darkening” of fluorescence of the tissue. Still another embodimentuses a subset of the union of the two optimal windows above. FIGS. 1A,1B, 2A, 2B, 3A, 3B, 3C, and 3D demonstrate “whitening” of reflectanceand “darkening” of fluorescence as a function of wavelength and timefollowing application of acetic acid. The maximum change observed in theCIN 2/3 group is determined from the data shown in FIGS. 2A, 2B, 3C, and3D. Here, the peak “darkening” of the fluorescence data lags peak“whitening” of the reflectance data. From the reflectance data, thewindow for peak whitening lies between about 30 s and about 110 sfollowing the application of acetic acid with a maximum at about 70 s.In one embodiment, the peak whitening window lies between about 30 s andabout 130 s; and in another embodiment from about 20 s to about 180 s.For fluorescence, the peak “darkening” window lies between about 50 sand about 150 s with a minimum at about 80 s. In one embodiment, thepeak darkening window lies between about 60 s and about 220 s. Peak“whitening” for the non-CIN reflectance spectra is less intense butsimilar in shape to that found for CIN 2/3. Peak darkening in non-CINfluorescence appears later than in CIN 2/3 fluorescence.

[0055]FIGS. 4A and 4B depict the influence of acetic acid on reflectanceand fluorescence intensities at about 425 nm and about 460 nm,respectively, for various reference tissue classes. These classesinclude CIN 2/3 (curves 406 and 454), CIN 1 (curves 408 and 456),metaplasia TT016 and TT017 (curves 410 and 458), normal columnar TT022(curves 412 and 460) and normal squamous TT025 (curves 414 and 462)tissues, as shown in FIGS. 4A and 4B. In general, the reflectance curvesof FIG. 4A show some distinct differences with tissue type, with CGN 2/3tissue (curve 406) having the largest change. Columnar epithelial tissue(curve 412) shows rapid relatively intense whitening followed by rapidrecovery while squamous epithelial tissue (curve 414) has a weak, slowresponse with very little recovery. Metaplastic tissues (curve 410) andtissue with CIN 1 (curve 408) have similar behavior with a relativelyfast increase and decay. The acetowhitening response of all tissuegroups ride on top of a slowing, increasing background, therebysuggesting a secondary response to acetic acid. This secondary responseis most distinct in the CIN 1 group and appears to be the predominantresponse in the normal squamous group.

[0056] The magnitude of the acetodarkening effect for fluorescence issimilar independent of tissue type, as shown in FIG. 4B. The time toreach a minimum fluorescence is different for different tissue classes,with normal squamous tissue (curve 462) having the slowest response andnormal columnar tissue (curve 460) having the fastest response. Theresponse for CIN 2/3 (curve 454), CIN 1 (curve 456), and metaplastictissues (curve 458) are very similar. There is partial recovery from theacetic acid effect in the CIN 2/3 group (curve 454).

EXAMPLE 3 Using a Discrimination Function to Determine Optimal Windowsfor Obtaining Diagnostic Optical Data.

[0057] An embodiment of the invention comprises determining an optimumwindow for obtaining diagnostic spectral data using fluorescence and/orreflectance time-response data as shown in the above figures, and asdiscussed above. In one embodiment, an optimum window is determined bytracking the difference between spectral data of various tissue typesusing a discrimination function.

[0058] In one embodiment, the discrimination function shown below inEquation (1) is used to extract differences between tissue types:$\begin{matrix}{{D(\lambda)} = \frac{{\mu \left( {{test}\quad (\lambda)} \right)}_{NEDPATH1} - {\mu \left( {{test}(\lambda)} \right)}_{CIN23ALL}}{\sqrt{{\sigma^{2}\left( {{test}\quad (\lambda)} \right)}_{NEDPATH1} + {\sigma^{2}\left( {{test}(\lambda)} \right)}_{CIN23ALL}}}} & (1)\end{matrix}$

[0059] The quantity μ corresponds to the mean optical signal and σcorresponds to the standard deviation. In one embodiment, the opticalsignal includes diffuse reflectance. In another embodiment, the opticalsignal includes 337-nm fluorescence emission spectra. Other embodimentsuse fluorescence emission spectra at another excitation wavelength suchas 380 nm and 460 nm. In still other embodiments, the optical signal isa video signal, Raman signal, or infrared signal. Some embodimentscomprise using difference spectra calculated between different phases ofacetowhitening, using various normalization schema, and/or using variouscombinations of spectral data and/or image data as discussed above.

[0060] One embodiment comprises developing linear discriminant analysismodels using spectra from each time bin as shown in Table 1.Alternatively, nonlinear discriminant analysis models may be developed.Generally, models are trained using reflectance and fluorescence dataseparately, although some embodiments comprise use of both data types totrain a model. In exemplary embodiments discussed below, reflectance andfluorescence intensities are down-sampled to one value every 10 nmbetween 360 and 720 nm. A model is trained by adding and removingintensities in a forward manner, continuously repeating the processuntil the model converges such that additional intensities do notappreciably improve tissue classification. Testing is performed by aleave-one-spectrum-out jack-knife process.

[0061]FIG. 5 shows the difference between the mean reflectance spectrafor non-CIN 2/3 tissues (including CIN 1 and NED tissues) and CIN 2/3tissues at three times—at a time prior to the application of acetic acid(graph 502), at a time corresponding to maximum whitening (graph 520,about 60-80 seconds post-AA), and at a time corresponding to the latesttime period in which data was obtained (graph 550, about 160-180 secondspost-AA). Here, the time corresponding to maximum whitening wasdetermined from reflectance data, and occurs between about 60 secondsand 80 seconds following application of acetic acid. In the absence ofacetic acid, the reflectance spectra for CIN 2/3 (curve 510 of graph 502in FIG. 5) are on average lower than non-CIN 2/3 tissue (curve 508 ofgraph 502 in FIG. 5). Following the application of acetic acid, areversal is noted with CIN 2/3 tissues having higher reflectance thanthe other tissues. The reflectance of CIN 2/3 and non-CIN 2/3 tissuesincrease with acetic acid, with CIN 2/3 showing a larger relativepercent change (compare curves 522 and 524 of graph 520 in FIG. 5). Fromabout 160 s to about 180 s following acetic acid, the reflectance of CIN2/3 tissue begins to return to the pre-acetic acid state, while thereflectance of the non-CIN 2/3 group continues to increase (comparecurves 552 and 554 of graph 550 in FIG. 5)

[0062] In one embodiment, discrimination function ‘spectra’ arecalculated from the reflectance spectra of CIN 2/3 and non-CIN 2/3tissues shown in FIG. 5. In one example, discrimination function spectracomprise values of the discrimination function in Equation (1)determined as a function of wavelength for sets of spectral dataobtained at various times. FIG. 6 shows a graph 602 depicting thediscrimination function spectra evaluated using the diffuse reflectancedata of FIG. 5 obtained prior to application of acetic acid, and at twotimes post-AA. Curve 608 corresponds to the discrimination function 604evaluated as a function of wavelength 606 using non-CIN 2/3 data and CIN2/3 data obtained prior to application of acetic acid. Curve 610corresponds to the discrimination function 604 evaluated as a functionof wavelength 606 using non-CIN 2/3 data and CIN 2/3 data obtainedbetween about 60 and about 80 seconds after application of acetic acid;and curve 612 corresponds to the discrimination function 604 evaluatedas a function of wavelength 606 using non-CIN 2/3 data and CIN 2/3 dataobtained between about 160 and about 180 seconds after application ofacetic acid. Distinguishing between CIN 2/3 and non-CIN 2/3 tissuesusing reflectance data is improved with the application of acetic acid.Here, the largest differences (for example, the largest absolute valuesof discrimination function) are found from data measured from about 60 sto about 80 s post-acetic acid (curve 610), and these agree with thedifferences seen in the mean reflectance spectra of FIG. 5 (curves 522and 524 of graph 520 in FIG. 5).

[0063] Performing multivariate linear regression analysis addresseswavelength interdependencies in the development of a classificationmodel. An application of one embodiment comprises classifying datarepresented in the CIN 2/3, CIN 1, and NED categories in the AppendixTable into CIN 2/3 and non-CIN 2/3 categories by using classificationmodels developed from the reflectance data shown in FIG. 5. Here,reflectance intensities are down-sampled to one about every 10 nmbetween about 360 nm and about 720 nm. The model is trained by addingintensities in a forward-stepped manner. Testing is performed with aleave-one-spectrum-out jack-knife process. The result of this analysisshows which wavelengths best separate CIN 2/3 from non-CIN 2/3, as shownin Table 2 for an exemplary embodiment. TABLE 2 Forwarded selected bestreflectance wavelengths for classifying CIN 2/3 from non-CIN 2/3 spectraobtained at different times pre and post-AA. Time from AA (s) LDA ModelInput Wavelengths (nm) Accuracy −30 370 400 420 440 530 570 590 610 6630 420 430 450 600 74 50 360 400 420 430 580 600 74 70 360 370 420 430560 580 600 77 90 360 420 430 540 590 73 110 360 440 530 540 590 71 130360 420 430 540 590 71 150 370 400 430 440 540 620 660 690 720 72 170490 530 570 630 650 75

[0064] The two best models for separating CIN 2/3 and non-CIN 2/3 forthis embodiment include the model using reflectance data obtained atpeak CIN 2/3 whitening (from about 60 s to about 80 s) and the modelusing reflectance data from the latest time measured (from about 160 sto about 180 s post acetic acid). The first model uses input wavelengthsbetween about 360 and about 600 nm, while the second model uses morered-shifted wavelengths between about 490 and about 650 nm. This isconsistent with the behavior of the discrimination function spectrashown in FIG. 6.

[0065]FIG. 7 demonstrates one method of determining an optimal windowfor obtaining reflectance spectral data in the diagnosis of the state ofhealth of a region of a sample as CIN 2/3 or non-CIN 2/3. FIG. 7 shows agraph 702 depicting the performance of the two LDA models described inTable 2 above as applied to reflectance spectral data obtained atvarious times following application of acetic acid 706. Curve 710 inFIG. 7 is a plot of the diagnostic accuracy of the LDA model based onreflectance spectral data obtained between about 60 and about 80 seconds(“peak whitening model”) as applied to reflectance spectra from the binsof Table 1, and curve 712 in FIG. 7 is a plot of the diagnostic accuracyof the LDA model based on reflectance spectral data obtained betweenabout 160 and about 180 seconds, as applied to reflectance spectra fromthe bins of Table 1. For the peak-whitening model, the highest accuracywas obtained at about 70 s, while accuracies greater than 70% wereobtained with spectra collected in a window between about 30 s and about130 s. The 160-180 s model had a narrower window around 70 s, butperforms better at longer times.

[0066]FIG. 8 shows the difference between the mean 337-nm fluorescencespectra for non-CIN 2/3 tissues (including CIN 1 and NED tissues) andCIN 2/3 tissues at three times—at a time prior to application of aceticacid (graph 802), at a time corresponding to maximum whitening (graph820, about 60 to about 80 seconds post-AA), and at a time correspondingto the latest time period in which data was obtained (graph 850, about160 to about 180 seconds post-AA). The time corresponding to maximumwhitening was determined from reflectance data, and occurs between about60 seconds and 80 seconds following application of acetic acid. In theabsence of acetic acid, the fluorescence spectra for CIN 2/3 tissue(curve 812 of graph 802 in FIG. 8) and for non-CIN 2/3 tissue (curve 810of graph 802 in FIG. 8) are essentially equivalent with a slightly lowerfluorescence noted around 390 nm for CIN 2/3 sites. Following theapplication of acetic acid, the fluorescence of CIN 2/3 and non-CIN 2/3tissues decrease, with CIN 2/3 showing a larger relative percent change(compare curves 824 and 822 of graph 820 in FIG. 8). From about 160 s toabout 180 s following acetic acid application, the fluorescence of CIN2/3 tissue shows signs of returning to the pre-acetic acid state whilethe fluorescence of the non-CIN 2/3 group continues to decrease (comparecurves 854 and 852 of graph 850 in FIG. 8).

[0067] In one embodiment, discrimination function ‘spectra’ arecalculated from the fluorescence spectra of CIN 2/3 and non-CIN 2/3tissues shown in FIG. 8. In one example, discrimination function spectracomprise values of the discrimination function in Equation (1)determined as a function of wavelength for sets of spectral dataobtained at various times. FIG. 9 shows a graph 902 depicting thediscrimination function spectra evaluated using the fluorescence data ofFIG. 8 obtained prior to application of acetic acid, and at two timespost-AA. Curve 908 corresponds to the discrimination function 904evaluated as a function of wavelength 906 using non-CIN 2/3 data and CIN2/3 data obtained prior to application of acetic acid. Curve 910corresponds to the discrimination function 904 evaluated as a functionof wavelength 906 using non-CIN 2/3 data and CIN 2/3 data obtainedbetween about 60 and about 80 seconds after application of acetic acid;and curve 912 corresponds to the discrimination function 904 evaluatedas a function of wavelength 906 using non-CIN 2/3 data and CIN 2/3 dataobtained between about 160 and about 180 seconds after application ofacetic acid. Distinguishing between CIN 2/3 and non-CIN 2/3 tissuesusing fluorescence data is improved with the application of acetic acid.Here, the largest absolute values are found from data measured withinthe range of about 160-180 s post-acetic acid (curve 912), and theseagree with the differences seen in the mean fluorescence spectra of FIG.8 (curves 852 and 854 of graph 850 in FIG. 8).

[0068] Performing multivariate linear regression analysis addresseswavelength interdependencies in the development of a classificationmodel. An application of one embodiment comprises classifying datarepresented in the CIN 2/3, CIN 1, and NED categories in the AppendixTable into CIN 2/3 and non-CIN 2/3 categories by using classificationmodels developed from the fluorescence data shown in FIG. 8.Fluorescence intensities are down-sampled to one about every 10 nmbetween about 360 and about 720 nm. The model is trained by addingintensities in a forward manner. Testing is performed by aleave-one-spectrum-out jack-knife process. The result of this analysisshows which wavelengths best separate CIN 2/3 from non-CIN 2/3, as shownin Table 3 for an exemplary embodiment. TABLE 3 Forwarded selected best337-nm fluorescence wavelengths for classifying CIN 2/3 from non-CIN 2/3spectra obtained at different times pre and post-AA. Time from AA (s)LDA Model Input Wavelengths (nm) Accuracy −30 380, 430, 440, 610, 660,700, 710 61 30 370, 380, 390, 640 61 50 410 54 70 360, 390, 490, 580,590, 670 63 90 370, 380, 420, 460, 500, 560, 660 64 110 360, 390, 400,710 51 130 370 53 150 370, 380, 440, 620, 640, 700 65 170 370, 480, 510,570, 600, 700, 720 76

[0069] The two best models for separating CIN 2/3 and non-CIN 2/3 forthis embodiment include the models using data obtained at peak CIN 2/3whitening (60-80 s) and the model using data at the latest time measured(160-180 s post acetic acid). The first model uses input wavelengthsbetween about 360 and about 670 nm, while the second model useswavelengths between about 370 and about 720 nm. This is consistent withthe discrimination function spectra shown in FIG. 9.

[0070]FIG. 10 demonstrates one method of determining an optimal windowfor obtaining fluorescence spectral data in the diagnosis of the stateof health of a region of a sample as CIN 2/3 or non-CIN 2/3. FIG. 10shows a graph 1002 depicting the performance of the two LDA modelsdescribed in Table 3 above as applied to fluorescence spectral dataobtained at various times following application of acetic acid 1006.Curve 1010 in FIG. 10 is a plot of the diagnostic accuracy of the LDAmodel based on fluorescence spectral data obtained between about 60 andabout 80 seconds (“peak whitening model”) as applied to fluorescencespectra from the bins of Table 1, and curve 1012 in FIG. 10 is a plot ofthe diagnostic accuracy of the LDA model based on fluorescence spectraldata obtained between about 160 and about 180 seconds, as applied tofluorescence spectra from the bins of Table 1. The accuracies of thesemodels vary depending on when the fluorescence spectra are recordedrelative to the application of acetic acid, as shown in FIG. 10. Thepredictive ability of the fluorescence models in FIG. 10 tend to be lessthan that of the reflectance models in FIG. 7. Accuracies greater than70% are obtained with spectra collected after about 160 seconds post-AA.

[0071] Another embodiment comprises classifying data represented in theCIN 2/3, CIN 1, and NED categories in the Appendix Table into CIN 2/3and non-CIN 2/3 categories by using fluorescence divided by diffusereflectance spectra. Models are developed based on time post aceticacid. Ratios of fluorescence to reflectance are down-sampled to oneevery 10 nm between about 360 and about 720 nm. The model is trained byadding intensities in a forward manner. Testing is performed by aleave-one-spectrum-out jack-knife process. For this analysis, the modelis based on intensities at about 360, 400, 420, 430, 560, 610, and 630nm. In general, the results are slightly better than a model based onfluorescence alone. Improved performance is noted from spectra acquiredat about 160 s post acetic acid.

[0072]FIG. 11 shows a graph 1102 depicting the accuracy of three LDAmodels as applied to spectral data obtained at various times followingapplication of acetic acid. Curve 1110 in FIG. 11 is a plot of thediagnostic accuracy of the LDA model based on reflectance spectral dataobtained between about 60 and about 80 seconds (“peak whitening model”),also shown as curve 710 in FIG. 7. Curve 1112 in FIG. 11 is a plot ofthe diagnostic accuracy of the LDA model based on fluorescence spectraldata obtained between about 60 and about 80 seconds (“peak whiteningmodel”), also shown as curve 1010 in FIG. 10. Curve 1114 in FIG. 11 is aplot of the diagnostic accuracy of the LDA model based on fluorescenceintensity divided by reflectance, as described in the immediatelypreceding paragraph.

[0073] The exemplary embodiments discussed above demonstrate that theability to distinguish between non-CIN 2/3 and CIN 2/3 fluorescence andreflectance spectra is improved with the application of acetic acid orother contrast agent. For the peak-whitening LDA model using reflectancedata, the highest accuracy for the exemplary applications of theembodiments discussed herein is obtained at about 70 s followingintroduction of acetic acid, while accuracies greater than about 70% areobtained with spectra collected in a window between about 30 s and about130 s. The predictive ability of the fluorescence models in the examplesabove tend to be less than that of the reflectance models for theexamples discussed above. Accuracies greater than 70% are obtained withfluorescence at times greater than about 160 s post acetic acid. Theintensity of fluorescence continuously drop over the measurement periodin the non-CIN groups while partial recovery occurs at all 3 emissionwavelengths in the CIN 2/3 group, suggesting that fluorescence spectraldata obtained at times greater than about 180 s is useful in diagnosingCIN 2/3.

EXAMPLE 4 Other Kinetics-based Approaches for Obtaining DiagnosticOptical Data within an Optimal Window

[0074] As an alternative to the techniques discussed above, otherkinetics-based approaches may be used to determine classification modelsand, hence, corresponding optimum windows for classification of tissuesamples. The time response of fluorescence intensity or the timeresponse of reflectance following application of contrast agent, asshown in FIG. 3 and FIG. 4, may be curve-fitted to determine one or moreparameters sensitive to a curve feature of interest. For example, aparameter sensitive to a local minimum may be determined for a given setof fluorescence response data. In one embodiment, a parameter isdetermined by curve-fitting fluorescence time response data to asigmoidal function. Values of the parameter and/or goodness-of-fit dataare then used to develop a statistical model for classifying a sample interms of a characteristic of the sample, such as its state of health.The model is built using reference data with known states of health.Then, the time response of spectral intensity of a test sample withunknown state of health following application of a contrast agent isobtained. By curve-fitting this response data, values of the indicatedparameter(s) may be obtained, and the model may be used to eitherdirectly determine the characteristic of the test sample, or to indicatean optimal window in which spectral data should be obtained and used toaccurately classify the tissue. In one embodiment, the parameterdetermined by curve-fitting spectral time response curves is not useddirectly to classify the tissue, but is used to determine an optimalwindow. The parameter indicates a window of time in which one or morecomplete sets of spectral and/or video data should be obtained foraccurate diagnosis of the tissue.

EXAMPLE 5 Using a Relative Change or Rate-of-change Trigger to ObtainDiagnostic Optical Data

[0075] An embodiment of the invention comprises determining and using arelative amplitude change and/or rate of amplitude change as a triggerfor obtaining diagnostic optical data from a sample. The trigger canalso be used to determine an optical window of time for obtaining suchdiagnostic optical data. By using statistical and/or heuristic methodssuch as those discussed herein, it is possible to relate moreeasily-monitored relative changes or rates-of-change of one or moreoptical signals from a tissue sample to corresponding full spectrumsignals that can be used in characterizing the state of health of agiven sample. For example, by performing a discrimination functionanalysis, it may be found for a given tissue type that when the relativechange in reflectance at a particular wavelength exceeds a thresholdvalue, the corresponding full-spectrum reflectance can be obtained andthen used to accurately classify the state of health of the tissue. Inaddition, the triggers determined above may be converted into optimaltime windows for obtaining diagnostic optical data from a sample.

[0076]FIG. 12A shows how an optical amplitude trigger can be used todetermine an optimal time window for obtaining diagnostic optical data.The graph 1200 in FIG. 12A plots the normalized relative change of meanreflectance signal 1202 from tissue samples with a given state of healthas a function of time following application of acetic acid 1204. Themean reflectance signal determined from CIN 1, CIN 2, and Metaplasiasamples are depicted in FIG. 12A by curves 1210, 1208, and 1212,respectively. Here, it was determined that when the normalized relativechange of mean reflectance reaches or exceeds 0.75, the image intensitydata and/or the full reflectance and/or fluorescence spectrum for agiven sample is most indicative of a given state of health of a sample.Thus, for CIN 2 samples, for example, this corresponds to a time periodbetween t₁ and t₂, as shown in the graph 1200 of FIG. 12A. Therefore,spectral and/or image data obtained from a tissue sample between t₁ andt₂ seconds following application of acetic acid can be used inaccurately determining whether or not CIN 2 is indicated for thatsample. In one embodiment, the relative change of reflectance of atissue sample at one or more given wavelengths is monitored, and whenthat relative change is greater than or equal to the 0.75 threshold,more comprehensive spectral and/or image data is obtained from thesample for purposes of characterizing whether or not the sample isindicative of CIN 2. FIG. 12A demonstrates the use of a threshold valueof relative optical signal change. In another embodiment, apredetermined range of values of the relative optical signal change isused such that when the relative signal change falls within thepredetermined range of values, additional spectral and/or image data iscaptured in order to characterize the sample.

[0077]FIG. 12B shows how a rate-of-change of optical amplitude triggercan be used to determine an optimal time window for obtaining diagnosticoptical data. The graph 1250 of FIG. 12B plots the slope of meanreflectance signal 1252 from tissue samples with a given state of healthas a function of time following application of acetic acid 1204. Theslope of mean reflectance is a measure of the rate of change of the meanreflectance signal. The rate of change of mean reflectance determinedfrom CIN 1, CIN 2, and Metaplasia samples are depicted in FIG. 12B bycurves 1258, 1256, and 1260, respectively. Here, it was determined thatwhen the absolute value of the slope has an absolute value less than orequal to 0.1, for example, in the vicinity of maximum reflectance, theimage intensity data and/or the full reflectance and/or fluorescencespectrum for a given sample is most indicative of a given state ofhealth of a sample. Thus, for CIN 2 samples, for example, thiscorresponds to a time period between t₁ and t₂ as shown in the graph1250 of FIG. 12B. Therefore, spectral and/or image data obtained from atissue sample between t₁ and t₂ seconds following application of aceticacid can be used in accurately determining whether or not CIN 2 isindicated for that sample. In one embodiment, the rate of change ofreflectance of a tissue sample is monitored at one or more givenwavelengths, and when that rate of change has an absolute value lessthan or equal to 0.1, more comprehensive spectral and/or image data isobtained from the sample for purposes of characterizing whether or notthe sample is indicative of CIN 2. FIG. 12B demonstrates use of a rangeof values of rate of optical signal change. Other embodiments use asingle threshold value.

EXAMPLE 6 Using Fluorescence, Reflectance and/or Image Time ResponseData to Diagnose Regions of Tissue

[0078] The figures discussed herein include time-response fluorescenceand reflectance data obtained following application of a contrast agentto tissue. In addition to an acetowhitening effect observed in thereflectance data, an “acetodarkening” effect is observed in thefluorescence data. For example, the fluorescence intensity of diseasedregions decreases to a minimum at about 70 s to about 130 s followingapplication of acetic acid. Thus, the presence of a minimum fluorescenceintensity within this window of time, as well as the accompanyingincrease in fluorescence from this minimum, may be used to indicatedisease. An embodiment of the invention comprises a method ofidentifying a characteristic of a region of a tissue sample includingapplying a contrast agent to a region of a tissue sample, obtaining atleast two values of fluorescence spectral intensity corresponding to theregion, determining whether the fluorescence spectral intensitycorresponding to the region increases after a predetermined timefollowing the applying step, and identifying a characteristic of theregion based at least in part on the determining step. In an embodiment,the obtaining step comprises obtaining a fluorescence spectral intensitysignal corresponding to the region as a function of time following theapplying step. In an embodiment, the method further comprisesdetermining whether the fluorescence spectral intensity corresponding tothe region decreases following the applying step, then increases afterthe predetermined time. In an embodiment, the predetermined time isabout 80 seconds.

[0079] An embodiment comprises a method of identifying a characteristicof a region of a tissue sample comprising applying a contrast agent to aregion of a tissue sample, obtaining a fluorescence spectral intensitysignal from the region of the tissue sample, determining an elapsed timefollowing the applying step at which the fluorescence spectral intensitysignal has a minimum value, and identifying a characteristic of theregion based at least in part on the elapsed time.

[0080] An embodiment comprises a method of identifying a characteristicof a region of a tissue sample comprising applying a contrast agent to aregion of a tissue sample, obtaining a reflectance signal from theregion of the tissue sample, determining a change in reflectancespectral intensity corresponding to the region of the tissue samplefollowing the applying step, and identifying a characteristic of theregion based at least in part on the change in reflectance spectralintensity. In an embodiment, the change in reflectance spectralintensity corresponding to the region comprises a change relative to aninitial condition of the region.

[0081] An embodiment comprises a method of identifying a characteristicof a region of a tissue sample comprising applying a contrast agent to aregion of a tissue sample, obtaining an optical signal from the regionof the tissue sample, determining a rate of change of the optical signalcorresponding to the region of the tissue sample, and identifying acharacteristic of the region based at least in part on the rate ofchange. In an embodiment, the optical signal comprises fluorescencespectral intensity at a given wavelength. In an embodiment, the opticalsignal comprises reflectance spectral intensity at a given wavelength.

[0082] An embodiment comprises a method of identifying a characteristicof a region of a tissue sample comprising applying a contrast agent to aregion of a tissue sample, obtaining a fluorescence signal from theregion of the tissue sample, obtaining a reflectance signal from theregion of the tissue sample, and identifying a characteristic of theregion based at least in part on the fluorescence signal and thereflectance signal.

[0083] An embodiment comprises obtaining an optical signal from 499regions, each region having a diameter of approximately 1 mm, coveringan area of tissue about 25 mm in diameter. An embodiment may alsocomprise obtaining a video image of about 480 by about 560 pixelscovering the same 25-mm diameter area of tissue. APPENDIX TABLE Numberof spectra (number of subjects) for each tissue class in each time binfor exemplary embodiments discussed herein. Time CIN 2/3 CIN 1Metaplasia TT_022¹ TT_025¹ NEDPath1¹ t ≦ 0 451 (62) 202 (46) 329 (77)202 (56) 294 (70) 816 (186)  0 < t ≦ 40 118 (21)  72 (14) 147 (33)  51(14) 113 (22) 307 (64)   40 < t ≦ 60 300 (47) 135 (31) 255 (58) 116 (32)230 (51) 597 (133)  60 < t ≦ 80 375 (54) 162 (39) 300 (68) 179 (42) 262(61) 731 (157)  80 < t ≦ 100 455 (60) 195 (42) 308 (70) 190 (49) 263(64) 752 (167) 100 < t ≦ 120 446 (60) 209 (45) 328 (76) 208 (52) 284(68) 811 (178) 120 < t ≦ 140 303 (44) 135 (30) 200 (48) 165 (43) 185(51) 545 (129) 140 < t ≦ 160 130 (18)  82 (17)  75 (19)  96 (23)  66(21) 232 (53)  160 < t ≦ 180  53 (9)  50 (9) 34 (9)  38 (12) 19 (6) 91(24) t > 180 14 (3) 26 (3) 33 (6) 23 (6) 30 (5) 86 (15)

Equivalents

[0084] While the invention has been particularly shown and describedwith reference to specific preferred embodiments, it should beunderstood by those skilled in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the invention as defined by the appended claims.

What is claimed is: 1-15 (cancelled)
 16. A method of identifying acharacteristic of a region of a tissue sample, the method comprising thesteps of: (a) applying a contrast agent to a region of a tissue sample;(b) obtaining at least one reflectance signal from the region of thetissue sample within a window of time, wherein the window of time beginsat about 30 seconds following application of the contrast agent and endsat about 130 seconds following application of the contrast agent; (c)obtaining a fluorescence signal from the region of the tissue samplewithin the window of time; and (d) identifying a characteristic of theregion based at least in part on the fluorescence signal and at leastone of the at least one reflectance signals. 17 The method of claim 16,the method further comprising the step of obtaining a video signal fromthe region of the tissue sample within the window of time. 18 The methodof claim 17, wherein step (d) comprises identifying a characteristic ofthe region based at least in part on the fluorescence signal, at leastone of the at least one reflectance signals, and the video signal. 19The method of claim 16, wherein step (b) comprises obtaining tworeflectance signals from the region of the tissue sample within thewindow of time. 20-41 (cancelled) 42 The method of claim 16, wherein thecharacteristic is a state of health. 43 The method of claim 42, whereinthe state of health comprises at least one of the group consisting ofnormal squamous tissue, normal columnar tissue, metaplasia, immaturemetaplasia, mature metaplasia, CIN1, CIN2, CIN3, CIS, and cancer. 44 Themethod of claim 16, wherein the identifying step (d) comprisesdetermining whether the region of the tissue sample is CIN 2+ tissue. 45The method of claim 16, wherein the contrast agent comprises aceticacid. 46 The method of claim 16, wherein the contrast agent is selectedfrom a group consisting of formic acid, propionic acid, butyric acid,Lugol's iodine, Shiller's iodine, methylene blue, toluidine blue, indigocarmine, indocyanine green, and fluorescein. 47 The method of claim 16,wherein the tissue sample comprises cervical tissue. 48 The method ofclaim 16, wherein the tissue sample comprises at least one of a groupconsisting of colorectal tissue, gastroesophageal tissue, urinarybladder tissue, lung tissue, and skin tissue. 49 The method of claim 16,wherein the tissue sample comprises epithelial cells. 50 The method ofclaim 16, wherein step (b) comprises obtaining the at least onereflectance signal from the region within a period of time that beginsat about 60 seconds following application of the contrast agent and endsat about 80 seconds following application of the contrast agent. 51 Themethod of claim 16, wherein step (b) comprises obtaining the at leastone reflectance signal from the region within a period of time thatbegins at about 70 seconds following application of the contrast agentand ends at about 130 seconds following application of the contrastagent. 52 The method of claim 16, wherein step (d) comprises identifyingthe characteristic of the region with an accuracy of at least about 70%.53 The method of claim 16, wherein step (b) comprises obtaining areflectance intensity from the region at each of a plurality ofwavelengths within the window of time. 54 The method of claim 16,wherein step (c) comprises obtaining a fluorescence intensity from theregion at each of a plurality of wavelengths within the window of time.55 The method of claim 54, wherein step (d) further comprises obtaininga video signal from the region within the window of time. 56 The methodof claim 16, wherein step (a) comprises applying a contrast agent to aplurality of regions of the tissue sample; step (b) comprises obtainingat least one reflectance signal from each of the plurality of regionswithin the window of time; step (c) comprises obtaining a fluorescencesignal from each of the plurality of regions within the window of time;and step (d) comprises identifying a characteristic of each of theplurality of regions. 57 The method of claim 16, wherein step (d)comprises identifying a characteristic of the region based substantiallyon at least one optical signal, obtained within the window of time. 58The method of claim 16, wherein the window of time begins at 20 secondsfollowing application of the contrast agent.